{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Custom Mean Functions: Meta-learning with GPs\n",
    "One of the advantages of Gaussian process is their flexibility as a modelling tool. For instance, if the modeller knows that there is an underlying trend in the data, they can specify a mean function that captures this trend. In this notebook, we will illustrate how to use GPflow to construct a custom neural network mean function for GPs that can capture complex trends. We will look at this functionality in the context of meta-learning; where a number of meta tasks are available at train time and the user wants to adapt a flexible model to new tasks at test time. For an in-depth discussion on this topic, please refer to *(Fortuin and Rätsch, 2019)*. This notebook reproduces section 4.2 of this paper."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import gpflow\n",
    "from gpflow.decors import params_as_tensors\n",
    "from gpflow.kernels import RBF\n",
    "from gpflow.likelihoods import Gaussian\n",
    "from gpflow.mean_functions import MeanFunction\n",
    "from gpflow.models import GPR\n",
    "from gpflow.params import Parameter, ParamList\n",
    "from gpflow.training import GradientDescentOptimizer\n",
    "\n",
    "from gpflow.test_util import notebook_niter\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generating the tasks\n",
    "To generate the meta and test tasks, we sample from a Gaussian process with an Squared Exponential covariance function and a sinusoidal mean function. Each task is a realisation of this process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_data(num_functions=10, N=1000):\n",
    "    jitter = 1e-6\n",
    "    Xs = np.linspace(-5.0, 5.0, N)[:, None]\n",
    "    kernel = RBF(input_dim=1, lengthscales=1.)\n",
    "    cov = kernel.compute_K_symm(Xs)\n",
    "    L = np.linalg.cholesky(cov + np.eye(N) * jitter)\n",
    "    epsilon = np.random.randn(N, num_functions)\n",
    "    F = np.sin(Xs) + np.matmul(L, epsilon)\n",
    "    return Xs, F"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We generate 10 tasks for illustration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xs, F = generate_data(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(Xs, F);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We generate the meta and test tasks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_meta_and_test_tasks(num_datapoints, num_meta, num_test):\n",
    "    N = 1000\n",
    "    Xs, F = generate_data(num_functions=num_meta + num_test, N=N)\n",
    "    meta_indices = [np.random.permutation(N)[:num_datapoints] for _ in range(num_meta)] \n",
    "    test_indices = [np.random.permutation(N)[:num_datapoints] for _ in range(num_test)]\n",
    "    meta = []\n",
    "    for i, mi in enumerate(meta_indices):\n",
    "        Y = F[mi, i][:, None] + 1e-1 * np.random.randn(num_datapoints, 1)\n",
    "        meta.append((Xs[mi], Y))\n",
    "    test = []\n",
    "    for i, ti in enumerate(test_indices):\n",
    "        Y = F[ti, num_meta + i][:, None] + 1e-1 * np.random.randn(num_datapoints, 1)\n",
    "        test.append(((Xs[ti], Y), (Xs, F[:, num_meta + i][:, None])))\n",
    "    return meta, test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_meta_tasks = 20\n",
    "num_test_tasks = 5\n",
    "num_datapoints = 5\n",
    "meta, test = generate_meta_and_test_tasks(num_datapoints, num_meta_tasks, num_test_tasks)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating the mean function\n",
    "To create a custom mean function in GPflow, we inherit from the `MeanFunction` class and implement a `__call__` method. We store the neural network parameter inside the object and implement the forward pass in `__call__`. Below is an example of how to do that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function for weight initialisation\n",
    "def xavier_weights(input_dim, output_dim):\n",
    "    \"\"\"\n",
    "    Xavier initialization for the weights of a NN layer\n",
    "    See:\n",
    "       Xavier Glorot and Yoshua Bengio (2010):\n",
    "       Understanding the difficulty of training deep feedforward neural networks.\n",
    "       International Conference on Artificial Intelligence and Statistics.\n",
    "    \"\"\"\n",
    "\n",
    "    xavier_std = (2./(input_dim + output_dim)) ** 0.5\n",
    "    return np.random.randn(input_dim, output_dim) * xavier_std\n",
    "\n",
    "\n",
    "# Neural network implementation\n",
    "class DeepNeuralNetworkMeanFunction(MeanFunction):\n",
    "    \"\"\"\n",
    "    Neural Network Mean Function\n",
    "    \"\"\"\n",
    "    def __init__(self, input_dim, output_dim, inner_dims=[100], inner_activation=tf.nn.tanh, seed=None, Ws=None, bs=None):\n",
    "        \"\"\"\n",
    "        :param input_dim: input dimension\n",
    "        :param output_dim: output dimension\n",
    "        :param inner_dims: dimension of inner layers\n",
    "        :param inner_activation: activation for inner layers\n",
    "        :param seed: random seed\n",
    "        :param mean_function: initialise the network with parameters from mean_function\n",
    "        \"\"\"\n",
    "\n",
    "        super().__init__()\n",
    "\n",
    "        self.seed = seed\n",
    "        self.rng = np.random.RandomState(self.seed)\n",
    "\n",
    "        self.input_dim = input_dim\n",
    "        self.output_dim = output_dim\n",
    "        self.inner_dims = inner_dims\n",
    "\n",
    "        self.inner_activation = inner_activation\n",
    "\n",
    "        if (Ws is None) and (bs is None):\n",
    "            self._construct_network()\n",
    "        else:\n",
    "            self.Ws = ParamList(Ws) \n",
    "            self.bs = ParamList(bs)\n",
    "\n",
    "    def _construct_network(self):\n",
    "        Ws, bs = [], []\n",
    "        dims = [self.input_dim, *self.inner_dims, self.output_dim]\n",
    "        for dim_in, dim_out in zip(dims[:-1], dims[1:]):\n",
    "            Ws.append(Parameter(xavier_weights(dim_in, dim_out), trainable=True))\n",
    "            bs.append(Parameter(np.zeros((1, dim_out)), trainable=True))\n",
    "\n",
    "        self.Ws, self.bs = ParamList(Ws), ParamList(bs)\n",
    "\n",
    "    @params_as_tensors\n",
    "    def __call__(self, X):\n",
    "        \"\"\"\n",
    "        Feedforward pass\n",
    "\n",
    "        :param X: NxD tensor\n",
    "        \"\"\"\n",
    "\n",
    "        forward = X\n",
    "\n",
    "        for i, (W, b) in enumerate(zip(self.Ws, self.bs)):\n",
    "            forward = tf.matmul(forward, W) + b\n",
    "            if i < len(self.bs) - 1:\n",
    "                forward = self.inner_activation(forward)\n",
    "\n",
    "        return forward"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building the GP meta-model\n",
    "Meta-learning boils down to learning a good prior that can generalise to new tasks with a small number of datapoints. This framework is prevelant in GP modelling, where we usually maximise the marginal likelihood to learn a good set of hyper-parameters that specify the GP prior. We will perform the same optimisation here; however, with sharing the hyper-parameters across all the meta-tasks. For simplicity, we will fix the kernel and likelihood parameters and only learn those for the mean function. Hence, our \"meta-learning\" procedure is to cycle through the meta-tasks continuously optimising their marginal likelihood until a convergence criteria is reached (here, we just implement a fixed humber of iterations over the tasks). To begin this process, first we create a utility function that takes in a task (X, Y) and a mean function and outputs a GP model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_model(X, Y, Ws, bs):\n",
    "    mean_function = DeepNeuralNetworkMeanFunction(1, 1, [64, 64], Ws=Ws, bs=bs)\n",
    "    kernel = RBF(input_dim=1)\n",
    "    model = GPR(X, Y, kern=kernel, mean_function=mean_function)\n",
    "    model.kern.trainable = False\n",
    "    model.likelihood.variance = 1e-2\n",
    "    model.likelihood.trainable = False\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we define the training loop for meta-learning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "\n",
    "def train_loop(meta_tasks, num_iter=5):\n",
    "    \"\"\"\n",
    "    Meta learning training loop\n",
    "    \n",
    "    :param meta_tasks: list of meta tasks.\n",
    "    :param num_iter: number of iterations of tasks set\n",
    "    :returns: a mean funciton object\n",
    "    \"\"\"\n",
    "    # Initialise mean function\n",
    "    Ws = None\n",
    "    bs = None\n",
    "    # Iterate for several passes over the tasks set\n",
    "    for iteration in range(num_iter):\n",
    "        ts = time.time()\n",
    "        print(\"Currently in meta-iteration {}\".format(iteration))\n",
    "        # Iterate over tasks\n",
    "        for task in meta_tasks:\n",
    "            X, Y = task\n",
    "            # we create each model in its own session and graph\n",
    "            with tf.Graph().as_default() as graph, tf.Session(graph=graph).as_default() as sess:                    \n",
    "                model = build_model(X, Y, Ws, bs)\n",
    "                GradientDescentOptimizer(1e-3).minimize(model, maxiter=notebook_niter(100, test_n=1))\n",
    "                # Extract optimal mean function params to be fed to the next model\n",
    "                Ws = list(model.mean_function.Ws.read_values().values())\n",
    "                bs = list(model.mean_function.bs.read_values().values())\n",
    "        print(\">>>> iteration took {} ms\".format(time.time() - ts))\n",
    "    return Ws, bs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Currently in meta-iteration 0\n",
      ">>>> iteration took 8.85409688949585 ms\n",
      "Currently in meta-iteration 1\n",
      ">>>> iteration took 8.86217451095581 ms\n",
      "Currently in meta-iteration 2\n",
      ">>>> iteration took 8.79325246810913 ms\n",
      "Currently in meta-iteration 3\n",
      ">>>> iteration took 8.622434616088867 ms\n",
      "Currently in meta-iteration 4\n",
      ">>>> iteration took 8.586118459701538 ms\n"
     ]
    }
   ],
   "source": [
    "Ws_optimal, bs_optimal = train_loop(meta)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we use the optimised mean function for all of the test tasks. Note, we do not do any further optimisation for the hyper-parameters in this step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_models = [build_model(X, Y, Ws_optimal, bs_optimal) for ((X, Y), (_, _)) in test]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model assessment\n",
    "We assess the performance of this procedure on the test tasks. For this, we use the mean squared error as a performance metric."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mean_squared_error(y, y_pred):\n",
    "    return np.mean((y - y_pred) ** 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Mg/AS51BWFEVHmZmZBAYGEhQURIsWLXjggQcAaNmyJT179gQgLCyM48eP06dPHwIDA/npp584f/48J06coFWrVvj6+iKEYMqUKUWWsXnzZh577DFAa9OvX79+iTHt2LHj5r7atm1Ly5Ytbyb2QYMGUb9+fRwcHGjfvj3nz5/H29ubM2fOMHPmTNauXYuzs7NJPpuKMkmvGCGENdoEtK2BL6SUe4pY52HgYYAWLVpUrKA2w7RH2YKCUR9C4glYOwdaD4K6jSpWrqLUAmWtWZvajTb2wpycnG7+LaVkyJAh/PbbrZW0oraravb29jf/tra2Ji8vDxcXFw4fPsy6deuYP38+f/zxBz/88EO1x3aDSS6eSinzpZSBgCfQXQjhX8Q630gpg6SUQe7upQ4nbBpW1jDmU8jN0JK7oigWqWfPnuzcuZPTp08DcP36daKiomjbti3nzp0jOjoa4F+J/4ZBgwbx1VdfAVp7eGpqKvXq1ePatWtFrt+vXz8WLlwIQFRUFDExMfj5+RUbX1JSEgaDgTvuuIM333yTgwcPVvi9moJJe8VIKVOAUGC4KfdbKe5toN9zEPE3RK3XOxpFUSrA3d2dBQsWcNddd9GxY0d69erFiRMncHBw4JtvvmHUqFF06dKFRo2KPiv/5JNPCA0NJSAggK5du3L8+HFcXV3p06cP/v7+zJ49+5b1H3/8cQwGAwEBAUyePJkFCxbcUlMv7OLFiwwYMIDAwECmTJnCO++8Y9L3X16VnhpPCOEO5EopU4QQjsB6YK6UcmVx2wQFBclqnWgjLwe+7KnduPTIdrCyvF6eilIVIiMjadeund5hKEUo6rsRQhyQUgaVtq0pMlwTIFQIcQTYB2woKanrwsYO+j8P8RFwcrXe0SiKolQpU/SKOSKl7Cyl7Cil9JdSvm6KwEzOf4I25MDWuapvu6IoNVrtaZOwttHa2i8fgZNr9I5GURSlytSexA7QcTK4eMEOfe8KUxRFqUq1K7Fb20D3RyB2L8Qd0jsaRVGUKlG7EjtA53vA1gn2fKN3JIqiKFWi9iV2h/oQeBdE/AXXk/SORlFqvfj4eO6++268vb3p2rUrvXr1YunSpdUeh5eXF0lJt+aEHj16EBgYSIsWLXB3dycwMJDAwMBbxpUpzebNmwkL+/+hs6ZMmcKyZctMFXaRal9iB+j2IOTnwJHf9Y5EUWo1KSVjx44lODiYM2fOcODAARYvXkxsbOy/1s3Ly6v2+Pbs2UN4eDivv/46kydPJjw8nPDwcLy8vG5ZLz8/v9h9FE7s1aF2JvZG7aBZEBz8RXV9VBQdbd68GTs7Ox599NGbr7Vs2ZKZM2cC2jC5t912GyEhIQwaNAgpJbNnz745PO7vv2uVs4ITbADMmDGDBQsWAFpN/JVXXqFLly4EBARw4sQJAJKTkxk6dCgdOnTgwQcfpDw3a+bl5dGgQQOeeuopOnbsyN69e/H09Lw5qmNYWBiDBw8mOjqa7777jvfff5/AwEB27doFQGhoKL1798bb27tKzk4sf2q8iuo8BVY+BXEHoVlXvaNRFP2tmQOXj5p2n40DYETxsxgdO3aMLl26lLiLgwcPcuTIERo2bMjff/9NeHg4hw8fJikpiW7duhEcHFxqGG5ubhw8eJAvv/ySDz74gO+++47XXnuNvn378vLLL7Nq1Sq+//77cr211NRUgoOD+fjjj4tdx8fHhwcffBA3NzeeeuopAL788ksSEhLYuXMnR48eZdKkSYwbN65cZZemdtbYAfzHg40jHPpV70gURTF64okn6NSpE926dbv52pAhQ2jYsCGgDad71113YW1tjYeHB/3792ffvn2l7nf8+PEAdO3a9Wb7+LZt224OzTtq1ChcXFzKFaudnV2FE/LYsWMRQtCxY0cuXrxYoX2UpPbW2B3qQ/vb4ehfMPQtsKujd0SKoq8SatZVpUOHDvz99983n3/xxRckJSURFPT/w6EUHL63ODY2NrdMn5eVlXXL8hsDeN0YZtcUHB0dEQXmhSgYQ+HyCys4oFhlx+sqSu2tsYPWHJOdBifMa2gbRaktQkJCyMrKujmkLlDiZNb9+vXj999/Jz8/n8TERLZt20b37t1p2bIlx48fJzs7m5SUFDZt2lRq2cHBwSxatAiANWvWcPXq1Uq9Fy8vLw4cOABwy49VScMDV5Xandhb9oEGLeDwYr0jUZRaSQjBsmXL2Lp1K61ataJ79+5MmzaNuXPnFrn+uHHj6NixI506dSIkJIT33nuPxo0b07x5cyZNmoS/vz+TJk2ic+fOpZb9yiuvsG3bNjp06MCSJUsqPgGQ0auvvsrjjz9Ot27dsLOzu/n67bffzh9//EHnzp1vXjytapUetrciqn3Y3pKs/y+EzYfZp8Gxgd7RKEq1UsP2mi+9h+21bO1uA0MunFKTcCiKUjOoxN4sCOo2hsjlekeiKIpiEiqxW1lBu9FwehPkFH/RRlFqKj2aY5WSVfY7UYkdoN0YbcLr6M16R6Io1crBwYHk5GSV3M2IlJLk5GQcHBwqvI/a24+9oJZ9wNEFIldotXdFqSU8PT2JjY0lMTFR71CUAhwcHPD09Kzw9iqxA1jbQpsRcHKVNvG1jV3p2yhKDWBra0urVq30DkMxMdUUc0O7MZCVCue26x2JoihKpajEfoPPQG0CDnUXqqIoFk4l9htsHcF3CESuBEPxYysriqKYO5XYC2o3Bq4nQGzpo8UpiqKYq0ondiFEcyFEqBDiuBDimBDiSVMEpgvfoWBtp/WOURRFsVCmqLHnAc9KKdsDPYEnhBDtTbDf6ufgDN4DtbtQVb9eRVEsVKUTu5TykpTyoPHva0Ak0Kyy+9VNu9GQEmP6mWQURVGqiUnb2IUQXkBnYE8Ryx4WQuwXQuw365sh/EaCsFLNMYqiWCyTJXYhRF3gb+ApKWVa4eVSym+klEFSyiB3d3dTFWt6Tm7anagqsSuKYqFMktiFELZoSX2hlHKJKfapq7ajITESkqP1jkRRFKXcTNErRgDfA5FSynmVD8kMtBmm/Xtqg75xKIqiVIApaux9gKlAiBAi3PgYaYL96qdhK3D1VZNvKIpikSo9CJiUcgcgSl3R0vgOhX3faWO029XROxpFUZQyU3eeFsd3MORnq0HBFEWxOCqxF6dlH7Cto5pjFEWxOCqxF8fGHlr11xK7ugtVURQLohJ7SXyHaHehJkXpHYmiKEqZqcReEt+h2r+qOUZRFAuiEntJGjSHRu0hap3ekSiKopSZSuylaTMMYnZr0+YpiqJYAJXYS+M7DAx5EL1Z70gURVHKRCX20nh2A0cX1RyjKIrFUIm9NNY24DMITm8Cg0HvaBRFUUqlEntZtB6kzYUaH6F3JIqiKKVSib0sfEK0f6M36RuHoihKGajEXhb1GoOHv9YcoyiKYuZUYi+r1oMhJgyy/jU5lKIoillRib2sfIeAIRfObtU7EkVRlBKpxF5WzXuAvbOaVUlRFLOnEntZWduC9wAtsavRHhVFMWMqsZeH7xC4FgcJx/WORFEUpViVnhqvVmk9WPv31Hrw6KBvLEqtIaXkWFwae89e4VhcGonp2VzLysXW2gpnBxtaujrh416XIC8XfBvVRZtfXqnNVGIvD+em4BGgNcf0fVrvaJQaLj07j192n+f3fTGcS84AwL2ePU0bOOLsYEN2noELVzLZcTqJrFztrmhXJzv6+7kzplNT+rZ2w9ZanZTXRiqxl5fvENj5CWSmgGMDvaNRaiApJX/uj+WdNZFczcill7crjw3wYYBfIzycHf61vsEguXA1gz1nr7A7OpkNx+NZcvAiDZ3smNDVk3t7tcTTRU3IXpsIqcOFwKCgILl///5qL9ckYvbAD0Nhwg/gf4fe0Sg1TGpmLk//Hs7mEwl083LhpVHtCWxevgpEdl4+W08msvTQRdYfj0dKydD2jXmkvzedW7hUUeRKdRBCHJBSBpW2nqqxl5dnEDg21EZ7VIldMaGY5Aym/biX2KsZvDKmPdN6eWFlVf72cnsba4Z2aMzQDo2JS8nkl7Dz/LY3hrXHLjPAz52nBrcp94+FYllM0gAnhPhBCJEghKj5o2RZWWtT5p1aD4Z8vaNRaoizSdeZ9PVurmbksOihntzXp1WFknphTRs48sLwtux8IYTnh/tx+EIKY7/Yyf0L9nHy8jUTRK6YI1NdWVkADDfRvsxfm2GQeRVi9+kdiVIDJFzL4p5vw8jJN/DbQz3p5tXQ5GU42dvw+IDWbH8hhNnD/Nh/7gojPtnGi0uPkngt2+TlKfoySWKXUm4DrphiXxbBJwSsbNTkG0qlZebk89BP+7makcvP93enXRPnKi2vrr0NTwxszdbZA7m3lxd/7LvAwA+28OWW02TlqjPQmqLa+kIJIR4WQuwXQuxPTEysrmKrhmMDaNELotbqHYli4V5aepQjF1P55M5A/JvVr7ZyXZzsePW2Dqx7Opie3q68t/YkQz7ayuYT8dUWg1J1qi2xSym/kVIGSSmD3N3dq6vYqtNmuHYHakqM3pEoFuqf8IssOXSRWSG+DO3QWJcYfNzr8t20IBY+2AM7ayvuX7Cfh3/eT+zVDF3iUUxD3b1QUX4jtH9Pqlq7Un4XUzL577IIurRowMyQ1nqHQ5/Wbqx5MpgXhrdl+6kkBs/bypdbTpOTp6aDtEQqsVeUqw+4+kLUGr0jUSyMlJL/LYsg3yD5eHJnbMzk7lA7GyseG+DDhmeCCfZ15721JxnxyTZ2RSfpHZpSTqbq7vgbsBvwE0LECiEeMMV+zZ7fcDi7XU2+oZTLhuPxbD6RwNOD29DC1fzuCPV0qcM39wbxw/QgcvIN3P3tHp7+PZykdNV7xlKYqlfMXVLKJlJKWymlp5Tye1Ps1+y1GaFNvhG9We9IFAuRkZPHayuO4+dRj+l9vPQOp0QhbT3Y8HR/Zoa0ZuWROAZ9uJXf9sZgMKhhq82deZwDWqrmPcDRBU6u1jsSxUL8sOMsF1MyeWOsv0UM0OVga82zQ/1Y82Q/2jaux3+WHGXi17s5cVmdpZoz8z+yzJm1DfiN1C6g5uXoHY1i5q5ez+HrrWcY0t6D7q1MfxNSVWrdqB6LH+7JBxM7cTbpOqM+3cE7qyPJyMnTOzSlCCqxV1a7MZCdCue26R2JYua+2hrN9Zw8Zg/z0zuUChFCMKGrJ5ue6c+ELp58ve0MQ+ZtY1Ok6vtublRiryzvgWDrBJEr9Y5EMWPxaVks2HWO8V08aeNRT+9wKsXFyY65EzryxyO9qGNnzQM/7eeRX/ZzKTVT79AUI4tK7PvOXWH54TjSsnL1DuX/2TpAm6FwYpUaFEwp1vc7zpKXb2BWiK/eoZhM91YNWTWrH88P92NrVCKDP9x6830q+rKoxP77vgvM+u0QXV7fwJTv9vDTrnPmcYdcuzFwPQEu7NE7EsUMpWbksjDsPKM7NjXL7o2VYWdjxeMDWrPh6f50b9WQN1Ye5/YvdhJ+IUXv0Go1i5poI98gCb9wlfXH49l4PJ7oxOsAtG/izOD2Hgxt70GHps7VP+djdjp84Aud7oTRH1Vv2YrZ+2zTKT7cEMWaJ/tV+SBfepJSsibiMq+tOEbCtWym9GjJ7OF+ODvY6h1ajVHWiTYsKrEXdiYxnY2R8Ww4Hs+B81cxSGhS34Hh/o0ZGdCEri1cTDKmdZn8/aA2F+pzUWBjXz1lKmYvMyefPnM3E9i8AT9M76Z3ONXiWlYuH66P4ufd53Cta8//RrdnTMcmapJtE6gVib2g5PRsQk8msu7YZbZGJZKTZ6BRPXtGGJN8kFdDrKsyyZ/aCAvvgMkLod3oqitHsSiL9sTw4tKj/P5wT3p4u+odTrU6GpvKS8uOciQ2lX6+brw51p+Wrk56h2XRal1iLyg9O49NkfGsOXqZ0JMJZOcZcKtrz3B/D0YGNKG7V0PTj8+Rnwfz2mrD+U7+xbT7ViySlJIRn2xHCMHqWX1rZY013yD5New87687SW6+gRkDW/Nwf2/sbaz1Ds0i1erEXtD17DxCTyaw+uglNp9IICvXgKuTHcP8GzPSvwk9vU2Y5NfMgf3fw3OntDHblVpt79krTPp6N++OD+DO7i30DkdX8WlZvL7iOKuOXsLH3Yk3xwbQy6dVPPXLAAAgAElEQVR2ncGYgkrsRcjIyWPLycSbST4jJx+XOrYMae/BcP/G9GntVrmaxMWD8O1AGPMJdJ1usrgVy/TEooNsj0pkz4uDcbRTNVSA0JMJvPxPBBeuZHJHF09eHNkW17rqmlRZqcReiqzcfLacTGRNxCU2RyZwLTuPuvY2hLRtxHD/xvRv446TvU35diolfN4N6jaC+9T4MbVZfFoWfd7dzPTeXvx3dHu9wzErmTn5fB56im+2ncHJ3ob/jGjLxK7Nq6+jgwUra2IvZ+aqORxsrRnu35jh/o3JzstnV3Qy6yIus/54PMsPx2FvY0X/Nu4M92/MoLYe1K9Thi5bQkDHyRD6pjazUoPaffpdmy3aE0O+lEzp2VLvUMyOo501s4e1ZWxgM15aGsELfx/lrwOxvDUuwOLvyjUXtbbGXpy8fAP7zl1l3bHLrI24zOW0LGysBL18XBno14iBbRvRyq2EK/tXz8EnnWDQy9Dv2WqLWzEf+QZJ37mb8fWox8/3d9c7HLMmpeTPA7G8szqSa1l5PBTszawQX9V0VQzVFGMCBoPkyMVU1kZcZsPxyzdviGrpWocBbdwZ4NeInt6u/z4IfxgOGVfgiT1aLV6pVbZFJXLvD3v54u4ujOrYRO9wLMKV6zm8vTqSvw7E4uniyBu3+zOwbSO9wzI7KrFXgQtXMthyMoEtJxPZFZ1MZm4+9jZW9PB2pY+PK718XOnQtD7WB3+ElU/Dw1uhaaDeYSvVbMaig+w4ncSeFwepbn3lFHYmmf8ui+B0QjojAxrz8ugONK7voHdYZkMl9iqWlZvP3rNX2HIykW2nEjmdkA5APQcbQlraMi9mIqkB02kw9gN1UagWScnIoftbm7i7Rwteva2D3uFYpJw8A99uP8Onm05hYyWYEeLL/X291I8k6uJplXOwtSa4jTvBbdwBSLiWxe7oZMLOJLMrOpmNeYF0PfwHvSNC6OrThF4+rvTzdVN33tVwyw/HkZNvYGKQp96hWCw7GyueGNiaMR2b8vrKY8xde4Lf9sbw4si2DOvQuFbe6FVeqsZeRZKOrMdtyUQWN3meT6705FJqFgAtGtahr68bwb5u9PJxo76jGiCpJhn92XakhFWz+ukdSo2x/VQib66M5GT8NXq0asj/RrfHv1l9vcPShWqK0ZuUML8vGPKRj+3ibHIGO04nsS0qibAzyaRn52ElILB5A0b4N2FEQGM8XWrWkK61zfG4NEZ+up3XbuvAtN5eeodTo+TlG1i87wLzNkRxNSOHyUHNeXaoH+71atfNTSqxm4PwRbDsMZiyBFoPuvlybr6B8AspbD+VxKbIeI7FaRMDd2regLGBTRnf2bNs/eYVs/L26kh+3HmWvS8OxsXJTu9waqTUzFw+23SKBbvO4WBrzRMDW3NfHy8cbGtH+7tK7OYgLxs+DgAPf5i6pNjVzidfZ/XRy6w6GkfExTTsbawY06kp03t71dpTTktjMPZdb9fEme9ryfC8ejqTmM7bq0+wMTKe5g0deW6oH2M6Nq3xHRXKmthNMvqVEGK4EOKkEOK0EGKOKfZZI9jYQ/eHIHoTxB8vdrWWrk48NsCHlTP7sWpWXyZ09WTN0UuM/mwH9y/Yx2E1G43ZOxhzlbjULMZ0aqp3KLWCt3tdvpsWxK8P9KCuvS1PLg5nzOc72H4qUe/QzEKlE7sQwhr4AhgBtAfuEkKowTFuCHoAbBwh7Msyrd6haX3eGhdA2IuDeG5oGw7GXOX2L3by+MIDXExRkwWbqxXGYSgGt/fQO5Rapa+vG6tm9uXjyYGkZuYy9fu9TPluD0djU/UOTVemqLF3B05LKc9IKXOAxcDtJthvzVCnIQTeDUf+gPSEMm9Wz8GWGSG+7HghhGeGtGHziQQGf7iVL7ecJt9Q/c1nSvHy8g2sOnqJQe0aUbe8A8cplWZlJRjbuRmbnu3Py6PbcywulTGf72DWb4eISTaDOZF1YIrE3gy4UOB5rPG1WwghHhZC7BdC7E9MrGWnSz0fh/xsCPuq3JvWtbdh1iBfNj7Tn+A2bry39iSTv97NhSu184A1R3vOXiEpPYcxHVUzjJ7sbay5v28rtj4/kBkDW7P++GUGzdvCq8uPkXAtS+/wqpWJpxEqnpTyGyllkJQyyN3dvbqKNQ9urcF/gpbY0+IqtAtPlzp8PTWIjycHcvLyNUZ+sp31xy6bOFClIlYeicPJzlqNbWImnB1seW6YH1tnD2RiUHN+CTtP8HuhvL06kuT0bL3DqxamSOwXgeYFnnsaX1MKGvQyyHzY/FaldjO2czNWP9kPb3cnHvn1AF+EnkaPnk2KJifPwJqIywzt0LjWdLmzFB7ODrw9LoBNz/RnZEATvtt+hn7vhTJ37QmuXs/RO7wqZYrEvg/wFUK0EkLYAXcCy02w35rFpSX0eATCF8KlI5XaVfOGdfj9kV6M7tiU99ed5MWlR1W7u052nk4iJSOXMZ3UKI7mysvNiXmTAtnwTH+GtPdg/tZo+s7dzAfrTpKSUTMTfKUTu5QyD5gBrAMigT+klMcqu98aqd+zUMdVG/nRkF+pXTnYWvPpnYE8PsCH3/Ze4Nk/wsnLN5goUKWsVhyOo76jLX1b17LmRQvk416XT+7szPqnghnQthGfh56m39xQ5m2IqnEJ3iRt7FLK1VLKNlJKHyll5doaajJHFxj+DlzcD/t/qPTuhBA8P7wts4f5sSw8jmf+OIxB1dyrTVZuPuuPxzO8Q2PsbKrtcpVSSb4e9fji7i6sfaoffVq78emmU/R5dzNvr44kIa1mXGRVfbOqW8BEOPwbbHgFWgWDu1+ld/nEwNZYCcHctSdwqWPLq7d1UCPgVYMtJxNIz85TNyVZqLaNnZk/tSsnLqfx1ZZovtt+hgU7zzEhyJNHg31o4Wq5YzepakZ1EwJu/xLs6sAf90LOdZPs9rEBPjwc7M1Pu8/zRehpk+xTKdmKw5dwq2tHT++GeoeiVELbxs58cmdnQp8bwIQgT/7aH8uAD0J5cvEhTlxO0zu8ClGJXQ/OTeCO7yDxJCx7vNLt7Tf8Z0RbxnVuxgfro1gbobpCVqX07Dw2nYhnZEATbKzVf6OaoKWrE2+PC2D7CwN5sJ83G47HM/zj7TywYB+7TidZVO8zdUTqxXsADH0Dji+D1c9pw/xWkhCCd8YH0Kl5A579I5yTl69Vep9K0TZFxpOVa1DNMDWQh7MDL45sx645ITw9uA3hF1K4+7s9jPx0B3/uv0B2nmkqYlVJJXY99Z4JfZ7SLqSue9Ekyd3B1pqvp3Sljr0Nj/yyn/TsPBMEqhS24nAcTeo70LWFi96hKFWkQR07nhzsy845Icy9I4B8g4HZfx2hz7uhfLrplFnf7KQSu94Gvwo9HtUGCVs4AVIulLZFqRrXd+DzuzoTcyWDV5ernqemlpqRy9aoREZ3bFLjh4lVtMrS5G4tWPdUML880B3/Zs7M2xBFr3c3M+fvIxyPM792eJXY9SYEDH8XRrwP53fBlz1h33dgqFyf9B7erjwxsDV/HYhlxeGKDWOgFG3dscvk5kvVDFPLCCHo5+vOgvu6s/GZYCZ09WRZ+EVGfrqdO77axdJDsWTlmkczjZpow5xcPQcrnoQzW6BpF+gzC1oPAfu6Fdpdbr6BSV/v5nRCOmufCqZZA0eThltbTf1+DzFXMtjy3ADVrbSWS8nI4a8DsSzcE8PZpOu41LFlUlBz7u7RokomrlczKFkqKbUp9ba+CykxYG0HLftoDzdf7dHQB2wdyrS7mOQMhn+yjR6tGvLD9G4qEVVSUno23d/ayOMDWvPcsBLuQchKgwM/aj/SCZGQmaLdoObSErpMA//x2kQsSo1gMEh2RSfza9h5NkTGk2+QBLdx554eLQhp2whbE/WcUond0uXnwfmdcGo9nNoASScLLBTQoIWW5F19/z/hu7eFuv8eYfCHHWd5feVxPr2rM7ep5oNK+WX3Of73zzHWPRWMX+N6/14hPw92fgy7PoWsVPAIgMb+2lASmSkQuxeSosDJHbo/DL1nlflHWrEMl1OzWLwvhsV7L3A5LQu3uvbc0bUZk4Ka4+NesbPvG1Rir2my0yH5tPZIOgXJp7QEkRwNuQXGZvceCB3GQcdJYKs1veQbJOO/2kXslQw2PtNfTbRcCZO+3k1KRg7rn+7/74XJ0drk5Rf2gN9I6P88NO186zpSwplQCJsPp9Zp8+HeuUirySs1Sl6+gdCTify+7wKhJxPIN0iCWrrw39HtCWzeoEL7VIm9tjAY4Fqcluwv7NFGj0yJgfrNYcB/tNmbhCDyUhpjPtvBuM7NeH9iJ72jtkiXUjPp/e5mnhnchpmDfG9dmBAJP92mTagy8gPth7U0UethyYPa1IlTl4BHh6oJXNFdQloWfx+8yJ/7L/D11K74ehRxtlcG1TqZtaIjKyuo7wk+A2HAHHjyCExdBvUawz+Pw5/TIfMq7Zo480C/Vvx5IFZNjl1Bq45cQkoYXbg5KzUWfhkHwgoe2FC2pA7QZijct1brGfXjCDi/2/RBK2ahkbMDjw3wYdOz/Suc1MtDJfaaRggtyd+/Dga9AidWwld94dwOZob44lbXntdWHLOo26PNxYojl/Bv5kwrtwK9HbLSYOEkrals6pLyD+rm0R4eWK+1uf8yFs5uN23Qilmprs4LKrHXVFbW0O8ZLWnY2MOC0dQ9+A3PD/PjYEwKy1Xf9nKJSc7g8IWUW+c1zc/VBnJLOgmTf654U0qDFtoPcYOW8PsUrVlNUSpBDdtb0zXrCo9sg2WPwrr/MGG4NT83a8+7a04wtH1jHO3UdG5lseKI9kM4qmOBmZLWztEuhN72OfiEVK4AJze45w/4dhAsnAgPbgIn18rtUyleSox2Q2DcIW0e4vQE7fqIi5fWnbihN7ToCa4+ekdaIarGXhvY14UJP0Lb0VitfZ5P/Y5zKTWLBbvO6R2ZxVhxOI6uLV3wdDGO0X18uXaHcK8Z0GWqaQpx8YK7ftMSzZ/TTDbqp2IkJURvhsX3wMcdYekjcPAXSDwB1rbg0AAuHYYdH2nXpz7rAvP7wq7PtCY3C6Jq7LWFtS1M+AEWTcZ7z3+Z7vUx87dGc3ePFtR3tNU7OrN2Kv4aJy5f49Ux7bUXMq5odwg37axdxzCl5t1h1IewfAbs/kK7+1ipvIsHYNVzEHcQHOprA/B1ulO798Oq0Flrfi5cPQ9Ra+H4P7D+v7D1feh2v3bfQR3zH39f1dhrExt7uON7cHLnxevvYshM5bvtZ/SOyuytOHIJKwEjbzTDhL4FWSlaE4xNFdwT0HkKtB0Nm9+AyxGm339tkp+rzVb27SC4dglGfwTPRmlDZnt0+HdSB60S5NYaes+ABzfAQ6HQOgR2fKzV4g/+bPZnUyqx1zZOrjDhR+yux/Gz2098v+MMidfMd/hRvUkpWXk4jp7erjSq5wCXjmjDLHd7ULujtCoIAWM+0ZoGljwMuTVjHs5qlxYHP43R7gTuci88sReC7i//nb7NusDEBfDYTq2Gv3wmfNMf4sKrJGxTUIm9NmrRAwa9Quf07YwzbOTLLWoqveIci0vjTNJ1bSRHKWHN89qYLwNfrNqCndzg9s8h4RiEvlm1ZdVEF/bC/H7aD/H47+C2T8HBuXL79OgA01drTZrpCfBtCGx81Sx/eFVir616z4RWwfzXbjHrww4TX0NmZze1FUfisLESDO/QGI7+CTG7tXZ1x2qYYKPNMK2GuetzOLez6surKSJXaHcBOzjDw6HQcaLp9m1lBf53wBN7IPAu7ULr1/0gZo/pyjABldhrKyFg9Mc4iFxetFrAt9tUW3thBoNk5eFL9PV1w8UmG9b/T7tg2tlEvWDKYuibWj/3lU9DXk71lWupjv4Ff0zTatf3rS3/DWNl5egCt38BU5ZAbib8MAzW/sdkk9NXVqUSuxBiohDimBDCIIQodfwCxcy4+iD6z2aU9R4u7lnKlesqcRR0MOYqF1MytRExt74H6Ze1cWCsqrE+ZOcEI97TboIK+6L6yrVEhxfD3w9Cy95w7zKo51H1ZbYeBI/v1q65hH2pdY+M3lz15ZaiskdoBDAe2GaCWBQ99H6SnAatmS1+4aft6o7HgpYfjsPexophzfNhz3zodBd46lB/8Ruu9ZLZ+p52Y43ybydWayNrtgqGu/8A+6ofj+Um+3ow6gOYvkp7/ss4+Ot+SL1YfTEUUqnELqWMlFKeLH1NxWzZ2GE34k28rS5xPex70rJy9Y7ILOTlG1h99BKD2jXCKWyeduG0qi+YlmT4O9q/a/+jXwzmKiYM/roPmgRqQyDb1dEnDq++8NhuCJ4Nx5bB50FaE9qV6m/mrLZzSiHEw0KI/UKI/YmJidVVrFIWbYZzvUlPHpV/sni7mvwaYFd0MknpOUz2yYdDv0LX6Vpbt14atNDGdz+xEk6u1S8Oc5NwAhZNAudmcM+fFZ5G0mRsHSDkvzDrILS7TTt2Pu0M3w/T7lS+cqZaetGUmtiFEBuFEBFFPG4vT0FSym+klEFSyiB3d/eKR6yYnhA4jX4HN5FG3q4vycwx75svqsPyw3HUs7ehT8Jv2nC8/Z7VOyTo+QS4+cGa2ZCTUfr6Nd31ZC2p2zjA1KVaF1Fz4eIF47+GJw9DyP+0G9pWPasl+bNV33JdamKXUg6WUvoX8finyqNTqk+zLlxtNpA7Dav4Z1+U3tHoKis3n3URlxnX1hGbI79BwCRwblL6hlXNxk4bbiAlBnbM0zsafeXlaCNrXrts3jNQOTeF4Ofg8TB4dKd2t3JV3dhWgOruqNzUYNgcGop0krd+i8FQe8dr33IykWvZedxnHwp5mdDrCb1D+n+t+kHARNj5ae29kCqldtZyfod2E5ceF7TLSwgtoXeZqiX7KlbZ7o7jhBCxQC9glRBinWnCUvQgWvQkya0b47OXEnosVu9wdLPicBxNnAReZxZqw/F6tNc7pFsNflVrHtpg4gHILMXeb+HAAuj7dNlnq6plKtsrZqmU0lNKaS+l9JBSDjNVYIo+GgydQxNxhdMbv9U7FF2kZ+exMTKe2c2OItLjtWF5zU19T23Ux2NLzO6Oxyp3dps2Dn6bERDyst7RmC3VFKPcwsZ3EIn12jH06mIiLlzRO5xqt+H4ZbLz8hl+7W9o1L7yE2hUlT5PQr0mWpIzGPSOpnqkxWn9w119YPw31XujmIVRn4xyKyFwGvwCrazi2b/mR72jqXbLw+O4vd4p6lw9qbWtV9McleVm56Q1ycQdhEM/6x1N1cvP1SZmz8mAyb9WfkCvGk4lduVf6gTcTpJDS3rELiDuau3pVnf1eg7bTyUxq85acGqkXaQ0ZwGToGVfra0986re0VStja/ChT1w+2dVN/5LDaISu/JvVlZYBT9DO6sYdqxZpHc01WZNxGVayQv4pIZB94e1iUnMmZUVjHgXslJh2wd6R1N1ji+H3Z9r34n/HXpHYxFUYleK1LDHPVyx8aBV1A+15oalZYcu8nTdDUgbB224XEvQOECbcWnPfEisgaN7JEfDP09AsyAY+pbe0VgMldiVolnbcr3jNLpxjM07tusdTZWLSc7gzLmzDM3biuh0lzbTlKUY/KrW5r56ttbHu6bIzdSG4LWy1mYwqoppCGsoldiVYnmGPEQuNuSFfYusSQmjCEsPXWSKzQZsZI553ZBUFk5u2m3rZ7dqky/XFKtnQ/xRGP8tNGiudzQWRSV2pViibiPimg5jYPYm9kVd0DucKiOlZOXBaO632wRthoObr94hlV/X+8AjANa9ZDaTPVRK+CI49Is2UqLvEL2jsTgqsSslajxkBs4ik6iNNbfr48GYFLqmbsDZkGqeNySVhbWNNiZ4WqzlX0iNP64NmOXVDwaoYYorQiV2pUT2Xr2Ir+NL1/i/uFhDuz4uPRDDgzZryPfoqI2pbala9NQmA9n1GSRZ6ATlmSnw+z3a5BXjv9Xa15VyU4ldKZkQ2PV8iHZWMYRuXKl3NCaXnZdPwpH1tBYXse5txjckldXg18DWEdY8b3kXUg0GWPqINrjZxJ/MY0RNC6USu1Iqlx73kGnlhMuxn8nKrVldHzdHJjA+fx059i7QYZze4VRePQ+t+SJ6E5xYpXc05bPtPYhaC8PfhZa99I7GoqnErpTOvi4pvhMYLHezbs9RvaMxqdB94QyxPoBN12nmf0NSWXV/WBvnZu1/LOdC6sm1sOUd6HS3NjG0UikqsStl0njQ49iLPK7u+L7GdH28cj2H5mf/wAqJVbf79A7HdKxttAk5UmO0W/HNXXI0LHkYmnSC0fMsvznMDKjErpSJaNSWS649GZq5kv1na8actSsPnWeS1WautxioTWVWk7TsDT0eg73fwJktekdTvMwUWDRZ+zGa/Kt2fUCpNJXYlTJrOPAJmoorHNywWO9QKk1KyYXdf+EhUqjb5xG9w6kag18BV19Y9oQ2noy5MeTD3w/A1bMw6Rd9JwuvYVRiV8rMvt1Irtm64XNxKZdSM/UOp1IiLqYxIG0F6Q5Na+4NMLaOMG4+XIuDtS/qHc2tpISVT8PpjTDyA/Dqo3dENYpK7ErZWdsgO97JABHO0m0H9I6mUjZu304f62PYdL+/ZveV9gzSppAL/xVOrtE7mv+3+U04+BP0ew6CatD1DTOhErtSLs6978dGGMg7uMhiuz5m5OThdmIhedjg0H263uFUvf5zwMMfls+C68l6RwNh82H7B9BlGoT8V+9oaiSV2JXycfUhtVE3Rudv4p9Dljnh9bpDZ7idLaR4jYC67nqHU/Vs7GDc15CVAn9N12Yj0suRP2HtC9B2NIz+SPWAqSIqsSvl5tznQbytLhO+9R+L7Pp4eedCnEUGrgMe0zuU6tPYH8Z8ok0Grdfwvqc2wrJHtVmf7vi+ZjeB6UwldqXcRIdxZNk1ZEDaP+yONoNT+3I4HX+NvinLSHZqjWjZW+9wqlfg3dDnKTjwo9YNsjrF7oc/pkKjdnDXIrB1qN7ya5lKJXYhxPtCiBNCiCNCiKVCiAamCkwxYzb22HS5hxDrQ/y19aDe0ZTLjq3rCLA6h13PB2tnM8CgV8BvFKydA6c3VU+Z53bCz2OhbiO4529wqF895dZila2xbwD8pZQdgShAjbFZS9h0vRdb8ml0dgnnky3jtvWs3HwaRv5KlnCgXrd79A5HH1ZWMP4baNQB/pwOF/ZWbXmRK+HX8eDcFKav1sayUapcpRK7lHK9lDLP+DQM8Kx8SIpFcG9DTrMeTLLewoKdZ/WOpkw2HIhkqGEHV1uPBwdnvcPRj31duPt3bealn8fC2SqY+tBggC1ztSF4PTrAfWugfjPTl6MUyZRt7PcDZtRRVqlqdkHT8BaXOHNgA9eydOxpUUaJ23/EQeTSeNDjeoeiv/rNtGTboDksnKDdKGQq2enw572w5W3oeCdMX2VZc8jWAKUmdiHERiFERBGP2wus8xKQBywsYT8PCyH2CyH2JybWjLFGar0OY8m3rcftho38ud+8uz4ejrnCgGsria/fCdE4QO9wzEO9xlrSdfOFhZO0mZcq21smdj98G6INGTzsbe3OVzX+S7UrNbFLKQdLKf2LePwDIISYDowG7pEl9H2TUn4jpQySUga5u9eCvsO1gZ0T1oGTGW29hyU7j5BvMN+uj2Ebl+BtdRnn4Ef1DsW8OLnBtJXQYSxsfkNrD79ypvz7SU/UprP7fgjkpMOUJdqk4LXxArUZqGyvmOHA88BtUsqaOW+aUrKu92FHLj3S1rPheLze0RTpyvUcWp1bzHXrBjh2ukPvcMyPYwOtX/moeRCzBz7rqo3lfvVc6dsmnYZNr8PH/nBgAQTdD4+Hgc/Aqo5aKYFNJbf/HLAHNgjtlzlMSqmqRLVJY3+kZzfuvRjKrC13MayDB8LMamkrtu/jHnGAtI6P4lRTJtMwNSGg2wPagGih78CeryHsK2gcoE0q3aid1k1RCEi5oCX987sg3jjxSrvbtOEB3P10fRuKplKJXUrZ2lSBKJZLBD1Ay9hHcYzbze4zbent46Z3SDfl5BnI3fsjVkLSMLiGDs9rSg1awLivtCR98Gdtir0980EWGhfIrq7W22XYO1ozjnNTfeJVilTZGruiQIexyLVzuF+E8tWWYLNK7MsPnmNM/gauNg3GtaZNplGV6jeDgf/RHnnZcO0SZF/TxlCv3xzqNFTt52ZMDSmgVJ6tIyLwHgaxh8hT0URcNI9JHQwGSXToz3iIFBqGzNQ7HMtlY6/NMNU4AJoGal0XVVI3ayqxK6YRdD/WMo+p9lv5aku03tEAsOVkPKOuLyWtng+i9WC9w1GUaqMSu2Iabq2hVX+m229hbcRFziSm6x0R2zcsw9/qHHX6z1I1TKVWUYldMZ1uD1A/5zJDbcL5bPNpXUPZf+4KvRN/J9PWBZtOk3WNRVGqm0rsiun4jQTnZrzgEsqy8Iucir+mWyi/rdnMIOtD2PR4UN35qNQ6KrErpmNtCz0ewSvtAF3tLvDRxihdwth79gqdLi5GChtsez6sSwyKoieV2BXT6jIN7OryeqMtrD56WZceMj+s28Mkm63IgInaGOCKUsuoxK6YlmMD6DyFdskb8HVI44P1J6u1+J2nk+ga+zN25GET/Gy1lq0o5kIldsX0ej6GkAbebxHGlpOJbI2qntE88w2SL1bsYqrNRmTAJK2njqLUQiqxK6bn4gXtbqNT/BLaNRS8sfI4ufmGKi/27wOxDEpehJ3Ix3rA81VenqKYK5XYlarR50lEdhqft9rN6YR0Foadr9Lirmfn8eO63Uyx2YToNBlcfaq0PEUxZyqxK1WjWRdoNwbvUz8y0tuGDzdEEZ+WVWXFfbwxiklZf2En8hHBs6usHEWxBCqxK1Un5H+I3Ou8476RnDwDLy2NoIS5WCrsaGwqK3ccYIptKCLwLmjobfIyFMWSqMSuVB13Pwi8m/oRC3g5uB4bI+NZccaxUOYAAAbRSURBVOSSSYvIzTfwwt9HeNpxJTbCAKq2rigqsStVrP8cQHBXxiI6NW/AK/9EcCk102S7/3B9FFcvnWUCmxCB92gXbhWlllOJXalaDZpD94ewOvwbnw5yJDvPwIxFh0zSS2bLyQTmb43m8yZrtAM5+LlK71NRagKV2JWq1/cZsHWiZfiHzL2jIwfOX+WtVZGV2uX55Os888dhJridp+vV1drEyQ1amChgRbFsKrErVc/JFfrMghMrGVP/DA/0bcWCXef4dtuZCu3uyvUcpv+4DytDDm/b/qAl9P4vmDhoRbFcKrEr1aPXDKjfAlY9x4vDWjOqYxPeWh3Jgp1ny7WbhGtZ3P1tGBdTMlnW+RB2V0/ByA/Ark4VBa4olkcldqV62NWBke9BYiTWO+cxb1InhrT34NUVx3lrVdnuTD0el8bE+bs5n5zB4tuc8TzyGbQbA22GVcMbUBTLoRK7Un38RkDAJNj6HvaXD/HVPV2Y2rMl324/y7gvd3Io5mqRm2Xl5vNF6GnGfrmTzJx8Fk3rQJe9T4FDfRj1UTW/CUUxf6IqbhgpTVBQkNy/f3+1l6uYgcwUmN9X+/uBDeDchLURl3hpaQTJ13Po3KIBA9o0wtPFkczcfI7FpbIm4jIpGbkMbe/BO+M64LrmEYhcAff+A62C9X0/ilKNhBAHpJRBpa5XmcQuhHgDuB0wAAnAdCllXGnbqcRey8Udgh9HgUd7LTnbOZGenceiPedZdiiO45fSbq5az96G/n7u3NvLi+6tGsKGV2DnxzD0Teg9U8c3oSjVr7oSu7OUMs349yygvZTy0dK2U4ld4fhy+HMaNO8B9/wJ9vVuLsrIySMhLRs7GysaOztgZSVAStg6F7a8A13vg9EfqQmqlVqnrIn9/9q739Cq6jiO4+9PW2KtQmqC5LYcFMFYRf9MkP6oEZbDQXuUlEgPLCpQWES2qEdBEP150D+iHhQNJCgZRFEK0TP7o22BZWWZmRXNjOwPFOK3B+caS9bu5t25v+7vfl4w2Ln3cO/nx+DD2fecc29NM/ZjpV7RBtR/rmONqWc1DLwA+9+Hl/rhpy//eerUOa0sam/j7HmnFKX+52+w5bai1C9aA6sedambTaG11heQ9BCwFvgFWDbFfuuB9QBdXb6RxIDeG6FlDozcAU8vgStuh0vWwlnnFsV9+DsY2wzbn4Hfx2HZ/XDlIJzkc/5mU6k6ipG0DVgwyVNDETEyYb9NwNyIeLDam3oUY//y875izDI6XGy3zS9GL38cLLa7r4LlD0Dn5ekymv0P1GXGftwbdgFvRERvtX1d7DapQ3th77vFeCaOFh+/2zvgL80wq5husdc0ipF0XkR8UdnsB3bX8nrW5M7sLn4uXZc6iVlDq3XG/rCk8ykud9wHVL0ixszMylVTsUfEwGwFMTOz2eHLC8zMMuNiNzPLjIvdzCwzLnYzs8y42M3MMuNiNzPLTJLPY5c0TnHde6NpBw6mDlFHzbZe8JqbRaOu+ZyImF9tpyTF3qgkfTid23lz0WzrBa+5WeS+Zo9izMwy42I3M8uMi31mnksdoM6abb3gNTeLrNfsGbuZWWZ8xG5mlhkXu5lZZlzsJ0DSoKSQ1J46S9kkPSJpt6SPJW2RNC91prJIWinpM0l7JN2bOk/ZJHVKekfSJ5J2SdqQOlM9SGqR9JGk11NnKYuLfYYkdQLXAd+kzlInW4HeiLgQ+BzYlDhPKSS1AE8B1wM9wE2SetKmKt0RYDAieoAlwJ1NsGaADcCnqUOUycU+c48D9wBNcdY5It6OiCOVze1AR8o8JVoM7ImIryLiL2Azxdc9Zisivo+InZXff6Uou4VpU5VLUgewCng+dZYyudhnQFI/cCAixlJnSeRW4M3UIUqyENg/YftbMi+5iSQtAi4G3kubpHRPUByYHU0dpEy1fudpdiRtAxZM8tQQcB/FGCYrU605IkYq+wxR/Os+XM9sVj5JpwGvAhsj4nDqPGWR1Af8GBE7JF2TOk+ZXOzHiYhrJ3tc0gVANzAmCYqRxE5JiyPihzpGnHX/teZjJK0D+oAVke+NDweAzgnbHZXHsibpZIpSH46I11LnKdlSYLWkG4C5wBmSXo6ImxPnmnW+QekESfoauCwiGvET4qZN0krgMeDqiBhPnacsklopTg6voCj0D4A1EbErabASqThCeRE4FBEbU+epp8oR+90R0Zc6Sxk8Y7dqngROB7ZKGpX0bOpAZaicIL4LeIviJOIrOZd6xVLgFmB55W87WjmatQbnI3Yzs8z4iN3MLDMudjOzzLjYzcwy42I3M8uMi93MLDMudjOzzLjYzcwy8zcyQ80hqprdwgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean_squared_errors = []\n",
    "for i, test_task in enumerate(test):\n",
    "    (_, _), (Xs, F) = test_task\n",
    "    pred = test_models[i].predict_f(Xs)\n",
    "    plt.figure()\n",
    "    plt.plot(Xs, pred[0], label='Predictions')\n",
    "    plt.plot(Xs, F, label='Ground Truth')\n",
    "    mse = mean_squared_error(F, pred[0])\n",
    "    mean_squared_errors.append(mse)\n",
    "    plt.title(f\"Test Task {i + 1} | MSE = {mse}\")\n",
    "    plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Mean MSE over all 5 test tasks is 0.6406552218210799 +/- 0.36240625035328544\n"
     ]
    }
   ],
   "source": [
    "mean_mse = np.mean(mean_squared_errors)\n",
    "std_mse = np.std(mean_squared_errors) / np.sqrt(num_test_tasks)\n",
    "print(f\"The Mean MSE over all {num_test_tasks} test tasks is {mean_mse} +/- {std_mse}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We achieve comparable results to those reported in the paper.\n",
    "\n",
    "N.B. we only use 20 meta-tasks and 5 test-tasks for scalability, whereas the paper uses 1000 and 200 respectively. Hence, there might be some discrepancies in the results."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References:\n",
    "Fortuin, Vincent, and Gunnar Rätsch. \"Deep Mean Functions for Meta-Learning in Gaussian Processes.\" arXiv preprint arXiv:1901.08098 (2019)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
